The introduction of this framework was carried in [1]}, wherein the authors proposed FedAvg as the main algorithm. At each round of FedAvg, a small fraction of clients is selected and each selected client downloads the global model. The selected clients then proceed to update the model by running SGD using their local data, and upload the updated model to the server. Finally, a new global model is obtained by the server through averaging the updated models. Despite the fact that FedAvg is arguably the most widely adopted method of choice for FL, it does not address either system heterogeneity or statistical heterogeneity. Specifically, FedAvg does not explicitly handle the straggler problem in a heterogeneous network: the process may stall in the face of unreliable network connections or client unavailability. Moreover, FedAvg may diverge when data distributions across clients violate the IID (independent and identically distributed) assumption [1]}, [3]}, [4]}, [5]}.
